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  2. AZFinText - Wikipedia

    en.wikipedia.org/wiki/AZFinText

    Within this paper, the authors tested several different prediction models and linguistic textual representations. From this work, it was found that using the article terms and the price of the stock at the time the article was released was the most effective model and using proper nouns was the most effective textual representation technique.

  3. Stock market prediction - Wikipedia

    en.wikipedia.org/wiki/Stock_market_prediction

    The successful prediction of a stock's future price could yield significant profit. The efficient market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable. Others disagree and those with this viewpoint possess ...

  4. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    Predictive analytics, or predictive AI, encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

  5. Piotroski F-score - Wikipedia

    en.wikipedia.org/wiki/Piotroski_F-Score

    The highest possible Piotroski score is 9 and the lowest is 0. Higher the score better the value of the company's stock. F-score of 8–9 is considered to be strong. Alternatively, firms achieving the F-score of 0–2 are considered to be weak.

  6. Black–Litterman model - Wikipedia

    en.wikipedia.org/wiki/Black–Litterman_model

    In finance, the Black–Litterman model is a mathematical model for portfolio allocation developed in 1990 at Goldman Sachs by Fischer Black and Robert Litterman.It seeks to overcome problems that institutional investors have encountered in applying modern portfolio theory in practice.

  7. Positive and negative predictive values - Wikipedia

    en.wikipedia.org/wiki/Positive_and_negative...

    The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.